Instructions to use natihash/vit_base_patch16_clip_224.text_lora4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use natihash/vit_base_patch16_clip_224.text_lora4 with timm:
import timm model = timm.create_model("hf_hub:natihash/vit_base_patch16_clip_224.text_lora4", pretrained=True) - Transformers
How to use natihash/vit_base_patch16_clip_224.text_lora4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="natihash/vit_base_patch16_clip_224.text_lora4") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("natihash/vit_base_patch16_clip_224.text_lora4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e368ea69777d33ecb29a6f5934a65c8f012fdd4fffaac764a1196032ec8e7c9
- Size of remote file:
- 344 MB
- SHA256:
- 5cc858229b6c5dae0ce994df6bcba63727db7e490394c3a6e01892d5894e2766
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